{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "tf.keras序列问题.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPiUX/xJiFENUYfYh/WTwcY",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sunyingjian/study-tensorflow/blob/master/tf_keras%E5%BA%8F%E5%88%97%E9%97%AE%E9%A2%98.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D0TMvNuI_al0",
        "colab_type": "code",
        "outputId": "4600b6ed-dd8b-421d-9540-8f3d0cdbfd5a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        }
      },
      "source": [
        "#标准开场\n",
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "print('tensorflow:',format(tf.__version__))\n",
        "tf.config.list_physical_devices('GPU')\n",
        "tf.test.is_gpu_available()\n",
        "import numpy as np\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n",
            "tensorflow: 2.1.0\n",
            "WARNING:tensorflow:From <ipython-input-1-5df5e3fbd10e>:5: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.config.list_physical_devices('GPU')` instead.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "obIO52E-APqB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#导入电影评论数据，内置在keras中\n",
        "data=keras.datasets.imdb\n",
        "#我们设置一个maxword他是用来限制读取进的索引的最大数量的\n",
        "max_word=10000\n",
        "#加载数据，他是元组的形式\n",
        "(x_train,y_train),(x_test,y_test)=data.load_data(num_words=max_word)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ao9Mpv8tB_tt",
        "colab_type": "code",
        "outputId": "ea344ae9-f597-45e6-edb8-edafd6700862",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "#看下数据\n",
        "x_train.shape"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25000,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q_yyNDXGCHlf",
        "colab_type": "code",
        "outputId": "7c2087fb-8e4c-4e00-831d-fcf1e00bb837",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "x_train[0]#他这里是用整数代替这个单词"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              " 32]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uF88hvs-Dr_T",
        "colab_type": "code",
        "outputId": "04c4a50b-4cac-46ce-e038-fc82bed074ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "data.get_word_index()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'fawn': 34701,\n",
              " 'tsukino': 52006,\n",
              " 'nunnery': 52007,\n",
              " 'sonja': 16816,\n",
              " 'vani': 63951,\n",
              " 'woods': 1408,\n",
              " 'spiders': 16115,\n",
              " 'hanging': 2345,\n",
              " 'woody': 2289,\n",
              " 'trawling': 52008,\n",
              " \"hold's\": 52009,\n",
              " 'comically': 11307,\n",
              " 'localized': 40830,\n",
              " 'disobeying': 30568,\n",
              " \"'royale\": 52010,\n",
              " \"harpo's\": 40831,\n",
              " 'canet': 52011,\n",
              " 'aileen': 19313,\n",
              " 'acurately': 52012,\n",
              " \"diplomat's\": 52013,\n",
              " 'rickman': 25242,\n",
              " 'arranged': 6746,\n",
              " 'rumbustious': 52014,\n",
              " 'familiarness': 52015,\n",
              " \"spider'\": 52016,\n",
              " 'hahahah': 68804,\n",
              " \"wood'\": 52017,\n",
              " 'transvestism': 40833,\n",
              " \"hangin'\": 34702,\n",
              " 'bringing': 2338,\n",
              " 'seamier': 40834,\n",
              " 'wooded': 34703,\n",
              " 'bravora': 52018,\n",
              " 'grueling': 16817,\n",
              " 'wooden': 1636,\n",
              " 'wednesday': 16818,\n",
              " \"'prix\": 52019,\n",
              " 'altagracia': 34704,\n",
              " 'circuitry': 52020,\n",
              " 'crotch': 11585,\n",
              " 'busybody': 57766,\n",
              " \"tart'n'tangy\": 52021,\n",
              " 'burgade': 14129,\n",
              " 'thrace': 52023,\n",
              " \"tom's\": 11038,\n",
              " 'snuggles': 52025,\n",
              " 'francesco': 29114,\n",
              " 'complainers': 52027,\n",
              " 'templarios': 52125,\n",
              " '272': 40835,\n",
              " '273': 52028,\n",
              " 'zaniacs': 52130,\n",
              " '275': 34706,\n",
              " 'consenting': 27631,\n",
              " 'snuggled': 40836,\n",
              " 'inanimate': 15492,\n",
              " 'uality': 52030,\n",
              " 'bronte': 11926,\n",
              " 'errors': 4010,\n",
              " 'dialogs': 3230,\n",
              " \"yomada's\": 52031,\n",
              " \"madman's\": 34707,\n",
              " 'dialoge': 30585,\n",
              " 'usenet': 52033,\n",
              " 'videodrome': 40837,\n",
              " \"kid'\": 26338,\n",
              " 'pawed': 52034,\n",
              " \"'girlfriend'\": 30569,\n",
              " \"'pleasure\": 52035,\n",
              " \"'reloaded'\": 52036,\n",
              " \"kazakos'\": 40839,\n",
              " 'rocque': 52037,\n",
              " 'mailings': 52038,\n",
              " 'brainwashed': 11927,\n",
              " 'mcanally': 16819,\n",
              " \"tom''\": 52039,\n",
              " 'kurupt': 25243,\n",
              " 'affiliated': 21905,\n",
              " 'babaganoosh': 52040,\n",
              " \"noe's\": 40840,\n",
              " 'quart': 40841,\n",
              " 'kids': 359,\n",
              " 'uplifting': 5034,\n",
              " 'controversy': 7093,\n",
              " 'kida': 21906,\n",
              " 'kidd': 23379,\n",
              " \"error'\": 52041,\n",
              " 'neurologist': 52042,\n",
              " 'spotty': 18510,\n",
              " 'cobblers': 30570,\n",
              " 'projection': 9878,\n",
              " 'fastforwarding': 40842,\n",
              " 'sters': 52043,\n",
              " \"eggar's\": 52044,\n",
              " 'etherything': 52045,\n",
              " 'gateshead': 40843,\n",
              " 'airball': 34708,\n",
              " 'unsinkable': 25244,\n",
              " 'stern': 7180,\n",
              " \"cervi's\": 52046,\n",
              " 'dnd': 40844,\n",
              " 'dna': 11586,\n",
              " 'insecurity': 20598,\n",
              " \"'reboot'\": 52047,\n",
              " 'trelkovsky': 11037,\n",
              " 'jaekel': 52048,\n",
              " 'sidebars': 52049,\n",
              " \"sforza's\": 52050,\n",
              " 'distortions': 17633,\n",
              " 'mutinies': 52051,\n",
              " 'sermons': 30602,\n",
              " '7ft': 40846,\n",
              " 'boobage': 52052,\n",
              " \"o'bannon's\": 52053,\n",
              " 'populations': 23380,\n",
              " 'chulak': 52054,\n",
              " 'mesmerize': 27633,\n",
              " 'quinnell': 52055,\n",
              " 'yahoo': 10307,\n",
              " 'meteorologist': 52057,\n",
              " 'beswick': 42577,\n",
              " 'boorman': 15493,\n",
              " 'voicework': 40847,\n",
              " \"ster'\": 52058,\n",
              " 'blustering': 22922,\n",
              " 'hj': 52059,\n",
              " 'intake': 27634,\n",
              " 'morally': 5621,\n",
              " 'jumbling': 40849,\n",
              " 'bowersock': 52060,\n",
              " \"'porky's'\": 52061,\n",
              " 'gershon': 16821,\n",
              " 'ludicrosity': 40850,\n",
              " 'coprophilia': 52062,\n",
              " 'expressively': 40851,\n",
              " \"india's\": 19500,\n",
              " \"post's\": 34710,\n",
              " 'wana': 52063,\n",
              " 'wang': 5283,\n",
              " 'wand': 30571,\n",
              " 'wane': 25245,\n",
              " 'edgeways': 52321,\n",
              " 'titanium': 34711,\n",
              " 'pinta': 40852,\n",
              " 'want': 178,\n",
              " 'pinto': 30572,\n",
              " 'whoopdedoodles': 52065,\n",
              " 'tchaikovsky': 21908,\n",
              " 'travel': 2103,\n",
              " \"'victory'\": 52066,\n",
              " 'copious': 11928,\n",
              " 'gouge': 22433,\n",
              " \"chapters'\": 52067,\n",
              " 'barbra': 6702,\n",
              " 'uselessness': 30573,\n",
              " \"wan'\": 52068,\n",
              " 'assimilated': 27635,\n",
              " 'petiot': 16116,\n",
              " 'most\\x85and': 52069,\n",
              " 'dinosaurs': 3930,\n",
              " 'wrong': 352,\n",
              " 'seda': 52070,\n",
              " 'stollen': 52071,\n",
              " 'sentencing': 34712,\n",
              " 'ouroboros': 40853,\n",
              " 'assimilates': 40854,\n",
              " 'colorfully': 40855,\n",
              " 'glenne': 27636,\n",
              " 'dongen': 52072,\n",
              " 'subplots': 4760,\n",
              " 'kiloton': 52073,\n",
              " 'chandon': 23381,\n",
              " \"effect'\": 34713,\n",
              " 'snugly': 27637,\n",
              " 'kuei': 40856,\n",
              " 'welcomed': 9092,\n",
              " 'dishonor': 30071,\n",
              " 'concurrence': 52075,\n",
              " 'stoicism': 23382,\n",
              " \"guys'\": 14896,\n",
              " \"beroemd'\": 52077,\n",
              " 'butcher': 6703,\n",
              " \"melfi's\": 40857,\n",
              " 'aargh': 30623,\n",
              " 'playhouse': 20599,\n",
              " 'wickedly': 11308,\n",
              " 'fit': 1180,\n",
              " 'labratory': 52078,\n",
              " 'lifeline': 40859,\n",
              " 'screaming': 1927,\n",
              " 'fix': 4287,\n",
              " 'cineliterate': 52079,\n",
              " 'fic': 52080,\n",
              " 'fia': 52081,\n",
              " 'fig': 34714,\n",
              " 'fmvs': 52082,\n",
              " 'fie': 52083,\n",
              " 'reentered': 52084,\n",
              " 'fin': 30574,\n",
              " 'doctresses': 52085,\n",
              " 'fil': 52086,\n",
              " 'zucker': 12606,\n",
              " 'ached': 31931,\n",
              " 'counsil': 52088,\n",
              " 'paterfamilias': 52089,\n",
              " 'songwriter': 13885,\n",
              " 'shivam': 34715,\n",
              " 'hurting': 9654,\n",
              " 'effects': 299,\n",
              " 'slauther': 52090,\n",
              " \"'flame'\": 52091,\n",
              " 'sommerset': 52092,\n",
              " 'interwhined': 52093,\n",
              " 'whacking': 27638,\n",
              " 'bartok': 52094,\n",
              " 'barton': 8775,\n",
              " 'frewer': 21909,\n",
              " \"fi'\": 52095,\n",
              " 'ingrid': 6192,\n",
              " 'stribor': 30575,\n",
              " 'approporiately': 52096,\n",
              " 'wobblyhand': 52097,\n",
              " 'tantalisingly': 52098,\n",
              " 'ankylosaurus': 52099,\n",
              " 'parasites': 17634,\n",
              " 'childen': 52100,\n",
              " \"jenkins'\": 52101,\n",
              " 'metafiction': 52102,\n",
              " 'golem': 17635,\n",
              " 'indiscretion': 40860,\n",
              " \"reeves'\": 23383,\n",
              " \"inamorata's\": 57781,\n",
              " 'brittannica': 52104,\n",
              " 'adapt': 7916,\n",
              " \"russo's\": 30576,\n",
              " 'guitarists': 48246,\n",
              " 'abbott': 10553,\n",
              " 'abbots': 40861,\n",
              " 'lanisha': 17649,\n",
              " 'magickal': 40863,\n",
              " 'mattter': 52105,\n",
              " \"'willy\": 52106,\n",
              " 'pumpkins': 34716,\n",
              " 'stuntpeople': 52107,\n",
              " 'estimate': 30577,\n",
              " 'ugghhh': 40864,\n",
              " 'gameplay': 11309,\n",
              " \"wern't\": 52108,\n",
              " \"n'sync\": 40865,\n",
              " 'sickeningly': 16117,\n",
              " 'chiara': 40866,\n",
              " 'disturbed': 4011,\n",
              " 'portmanteau': 40867,\n",
              " 'ineffectively': 52109,\n",
              " \"duchonvey's\": 82143,\n",
              " \"nasty'\": 37519,\n",
              " 'purpose': 1285,\n",
              " 'lazers': 52112,\n",
              " 'lightened': 28105,\n",
              " 'kaliganj': 52113,\n",
              " 'popularism': 52114,\n",
              " \"damme's\": 18511,\n",
              " 'stylistics': 30578,\n",
              " 'mindgaming': 52115,\n",
              " 'spoilerish': 46449,\n",
              " \"'corny'\": 52117,\n",
              " 'boerner': 34718,\n",
              " 'olds': 6792,\n",
              " 'bakelite': 52118,\n",
              " 'renovated': 27639,\n",
              " 'forrester': 27640,\n",
              " \"lumiere's\": 52119,\n",
              " 'gaskets': 52024,\n",
              " 'needed': 884,\n",
              " 'smight': 34719,\n",
              " 'master': 1297,\n",
              " \"edie's\": 25905,\n",
              " 'seeber': 40868,\n",
              " 'hiya': 52120,\n",
              " 'fuzziness': 52121,\n",
              " 'genesis': 14897,\n",
              " 'rewards': 12607,\n",
              " 'enthrall': 30579,\n",
              " \"'about\": 40869,\n",
              " \"recollection's\": 52122,\n",
              " 'mutilated': 11039,\n",
              " 'fatherlands': 52123,\n",
              " \"fischer's\": 52124,\n",
              " 'positively': 5399,\n",
              " '270': 34705,\n",
              " 'ahmed': 34720,\n",
              " 'zatoichi': 9836,\n",
              " 'bannister': 13886,\n",
              " 'anniversaries': 52127,\n",
              " \"helm's\": 30580,\n",
              " \"'work'\": 52128,\n",
              " 'exclaimed': 34721,\n",
              " \"'unfunny'\": 52129,\n",
              " '274': 52029,\n",
              " 'feeling': 544,\n",
              " \"wanda's\": 52131,\n",
              " 'dolan': 33266,\n",
              " '278': 52133,\n",
              " 'peacoat': 52134,\n",
              " 'brawny': 40870,\n",
              " 'mishra': 40871,\n",
              " 'worlders': 40872,\n",
              " 'protags': 52135,\n",
              " 'skullcap': 52136,\n",
              " 'dastagir': 57596,\n",
              " 'affairs': 5622,\n",
              " 'wholesome': 7799,\n",
              " 'hymen': 52137,\n",
              " 'paramedics': 25246,\n",
              " 'unpersons': 52138,\n",
              " 'heavyarms': 52139,\n",
              " 'affaire': 52140,\n",
              " 'coulisses': 52141,\n",
              " 'hymer': 40873,\n",
              " 'kremlin': 52142,\n",
              " 'shipments': 30581,\n",
              " 'pixilated': 52143,\n",
              " \"'00s\": 30582,\n",
              " 'diminishing': 18512,\n",
              " 'cinematic': 1357,\n",
              " 'resonates': 14898,\n",
              " 'simplify': 40874,\n",
              " \"nature'\": 40875,\n",
              " 'temptresses': 40876,\n",
              " 'reverence': 16822,\n",
              " 'resonated': 19502,\n",
              " 'dailey': 34722,\n",
              " '2\\x85': 52144,\n",
              " 'treize': 27641,\n",
              " 'majo': 52145,\n",
              " 'kiya': 21910,\n",
              " 'woolnough': 52146,\n",
              " 'thanatos': 39797,\n",
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              " 'amerterish': 52368,\n",
              " 'rajni': 18523,\n",
              " 'leeves': 30610,\n",
              " 'macauley': 34767,\n",
              " 'seriously': 612,\n",
              " 'sugercoma': 52369,\n",
              " 'grimstead': 52370,\n",
              " \"'fairy'\": 52371,\n",
              " 'zenda': 30611,\n",
              " \"'twins'\": 52372,\n",
              " 'realisation': 17640,\n",
              " 'highsmith': 27664,\n",
              " 'raunchy': 7817,\n",
              " 'incentives': 40965,\n",
              " 'flatson': 52374,\n",
              " 'snooker': 35097,\n",
              " 'crazies': 16829,\n",
              " 'crazier': 14902,\n",
              " 'grandma': 7094,\n",
              " 'napunsaktha': 52375,\n",
              " 'workmanship': 30612,\n",
              " 'reisner': 52376,\n",
              " \"sanford's\": 61306,\n",
              " '\\x91doña': 52377,\n",
              " 'modest': 6108,\n",
              " \"everything's\": 19153,\n",
              " 'hamer': 40966,\n",
              " \"couldn't'\": 52379,\n",
              " 'quibble': 13001,\n",
              " 'socking': 52380,\n",
              " 'tingler': 21931,\n",
              " 'gutman': 52381,\n",
              " 'lachlan': 40967,\n",
              " 'tableaus': 52382,\n",
              " 'headbanger': 52383,\n",
              " 'spoken': 2847,\n",
              " 'cerebrally': 34768,\n",
              " \"'road\": 23490,\n",
              " 'tableaux': 21932,\n",
              " \"proust's\": 40968,\n",
              " 'periodical': 40969,\n",
              " \"shoveller's\": 52385,\n",
              " 'tamara': 25263,\n",
              " 'affords': 17641,\n",
              " 'concert': 3249,\n",
              " \"yara's\": 87955,\n",
              " 'someome': 52386,\n",
              " 'lingering': 8424,\n",
              " \"abraham's\": 41511,\n",
              " 'beesley': 34769,\n",
              " 'cherbourg': 34770,\n",
              " 'kagan': 28624,\n",
              " 'snatch': 9097,\n",
              " \"miyazaki's\": 9260,\n",
              " 'absorbs': 25264,\n",
              " \"koltai's\": 40970,\n",
              " 'tingled': 64027,\n",
              " 'crossroads': 19511,\n",
              " 'rehab': 16121,\n",
              " 'falworth': 52389,\n",
              " 'sequals': 52390,\n",
              " ...}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "InebM_QhILX5",
        "colab_type": "code",
        "outputId": "edaf9c5d-7fcf-4bb9-e0fd-5fb865ca0721",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#看评论长度是否相等\n",
        "[len(x) for x in x_train]"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " ...]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iKMLDCcpQBMw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train=keras.preprocessing.sequence.pad_sequences(x_train,300)\n",
        "x_test=keras.preprocessing.sequence.pad_sequences(x_test,300)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qnt6gIPbTH7F",
        "colab_type": "text"
      },
      "source": [
        "#英文转数字索引"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4lZaS1rRTL4_",
        "colab_type": "code",
        "outputId": "2a3bc3e8-8862-450f-b65f-5dc4824f13b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "test='i am a student'\n",
        "dict((word,test.split().index(word)) for word in test.split())"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'a': 2, 'am': 1, 'i': 0, 'student': 3}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i1kbpT6-crxK",
        "colab_type": "text"
      },
      "source": [
        "#建立模型"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4811aZdkcrDE",
        "colab_type": "code",
        "outputId": "bc26097b-db3a-4a61-acf7-6896ab413d86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        }
      },
      "source": [
        "model=keras.models.Sequential()\n",
        "model.add(layers.Embedding(10000,50,input_length=300))\n",
        "#经过这一层之后呢，这就变成了一个三维的数据，而且这层没有激活函数\n",
        "#我们只有把他们变成二维的才能add接下来的Dense层\n",
        "#所以我们在这里添加一个flatten层\n",
        "#现在的情况是这样的(2500,300,50)的向量\n",
        "model.add(layers.GlobalAveragePooling1D())\n",
        "model.add(layers.Dense(64,activation='relu', \n",
        "                       kernel_regularizer=regularizers.l2(0.01)),\n",
        "                    activity_regularizer=regularizers.l1(0.01))\n",
        "#这里就是在进行正则化操作\n",
        "model.add(layers.Dropout(0.5))\n",
        "model.add(layers.Dense(1,activation='sigmoid'))\n",
        "#因为这里是一个二分类问题所以他的最后一层输出\n",
        "#就是输出了1个out 激活函数采用的sigmoid\n",
        "model.summary()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-25798f740db7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGlobalAveragePooling1D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m model.add(layers.Dense(64,activation='relu', \n\u001b[0;32m----> 9\u001b[0;31m                        kernel_regularizer=regularizers.l2(0.01)),\n\u001b[0m\u001b[1;32m     10\u001b[0m                     activity_regularizer=regularizers.l1(0.01))\n\u001b[1;32m     11\u001b[0m \u001b[0;31m#这里就是在进行正则化操作\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'regularizers' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0rpxMkjgfVOb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#编译模型\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['acc'])\n",
        "#当然这里可以直接写adam,因为这是一个二分类所以用\n",
        "#binary_corssentropy"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R-ofpuntg2AT",
        "colab_type": "code",
        "outputId": "617ec925-8062-450f-81c5-f3bcee308cf3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        }
      },
      "source": [
        "#训练模型\n",
        "history=model.fit(x_train,y_train,epochs=15,batch_size=256,\n",
        "          validation_data=(x_test,y_test))\n",
        "#所以我们看到这个fit中首先是要有训练集的然后是epoch 还有我们一次扔多少进去\n",
        "#还有我们加了valiadton_data进行测试集的操作"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 25000 samples, validate on 25000 samples\n",
            "Epoch 1/15\n",
            "25000/25000 [==============================] - 4s 145us/sample - loss: 0.6769 - acc: 0.6162 - val_loss: 0.6296 - val_acc: 0.7753\n",
            "Epoch 2/15\n",
            "25000/25000 [==============================] - 3s 107us/sample - loss: 0.5070 - acc: 0.8099 - val_loss: 0.4004 - val_acc: 0.8531\n",
            "Epoch 3/15\n",
            "25000/25000 [==============================] - 3s 107us/sample - loss: 0.3349 - acc: 0.8727 - val_loss: 0.3193 - val_acc: 0.8723\n",
            "Epoch 4/15\n",
            "25000/25000 [==============================] - 3s 107us/sample - loss: 0.2666 - acc: 0.9005 - val_loss: 0.2944 - val_acc: 0.8802\n",
            "Epoch 5/15\n",
            "25000/25000 [==============================] - 3s 108us/sample - loss: 0.2298 - acc: 0.9151 - val_loss: 0.2832 - val_acc: 0.8838\n",
            "Epoch 6/15\n",
            "25000/25000 [==============================] - 3s 106us/sample - loss: 0.2018 - acc: 0.9280 - val_loss: 0.2819 - val_acc: 0.8863\n",
            "Epoch 7/15\n",
            "25000/25000 [==============================] - 3s 107us/sample - loss: 0.1797 - acc: 0.9374 - val_loss: 0.2889 - val_acc: 0.8839\n",
            "Epoch 8/15\n",
            "25000/25000 [==============================] - 3s 107us/sample - loss: 0.1626 - acc: 0.9440 - val_loss: 0.2943 - val_acc: 0.8830\n",
            "Epoch 9/15\n",
            "25000/25000 [==============================] - 3s 106us/sample - loss: 0.1494 - acc: 0.9490 - val_loss: 0.3014 - val_acc: 0.8828\n",
            "Epoch 10/15\n",
            "25000/25000 [==============================] - 3s 107us/sample - loss: 0.1345 - acc: 0.9568 - val_loss: 0.3141 - val_acc: 0.8799\n",
            "Epoch 11/15\n",
            "25000/25000 [==============================] - 3s 107us/sample - loss: 0.1257 - acc: 0.9592 - val_loss: 0.3257 - val_acc: 0.8779\n",
            "Epoch 12/15\n",
            "25000/25000 [==============================] - 3s 107us/sample - loss: 0.1140 - acc: 0.9655 - val_loss: 0.3399 - val_acc: 0.8771\n",
            "Epoch 13/15\n",
            "25000/25000 [==============================] - 3s 107us/sample - loss: 0.1062 - acc: 0.9690 - val_loss: 0.3618 - val_acc: 0.8696\n",
            "Epoch 14/15\n",
            "25000/25000 [==============================] - 3s 108us/sample - loss: 0.0995 - acc: 0.9706 - val_loss: 0.3687 - val_acc: 0.8714\n",
            "Epoch 15/15\n",
            "25000/25000 [==============================] - 3s 109us/sample - loss: 0.0909 - acc: 0.9746 - val_loss: 0.3865 - val_acc: 0.8696\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uRMyn2jwT6Tu",
        "colab_type": "text"
      },
      "source": [
        "#对history中的数据进行绘图"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BKvwQ_nKUFDP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a3448ae3-8d21-4dbb-dfbd-fe592853119b"
      },
      "source": [
        "history.history.keys()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'acc', 'val_loss', 'val_acc'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7_fcRvp4VwCB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "5f12eddd-d2ff-41d9-9896-7deb4f3786a5"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "#魔术命令\n",
        "#看一下值的变化\n",
        "plt.plot(history.epoch,history.history['loss'],'r')\n",
        "#这里是epoch做横坐标，loss损失函数做纵坐标并且是红色的\n",
        "plt.plot(history.epoch,history.history['val_loss'],'b--')\n",
        "#这里是测试机的"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7efbb27f7198>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7ZrbIzOab2Qtm1q3hS5WMdPjh3lr/4AO/efrqq3DCCTB4MNx9N2zeHHWFIjmj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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v6O-SvBFbyK8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "0d0c2565-ffac-4a80-a196-73eef56ad2fe"
      },
      "source": [
        "plt.plot(history.epoch,history.history['acc'],'r')\n",
        "plt.plot(history.epoch,history.history['val_acc'],'b--')"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7efbb3d197b8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XobZW7fMiUhJKM+hTJ2LHjMlvHSIiraB0g36ffWISESlypRv0arYRkRJRekH/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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}